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Language Modelling

A language model is a model of natural language. Language models are useful for a variety of tasks, including speech recognition, machine translation, natural language generation (generating more human-like text), optical character recognition, route optimization, handwriting recognition, grammar induction, and information retrieval.

Large language models (LLMs), currently their most advanced form, are predominantly based on transformers trained on larger datasets (frequently using words scraped from the public internet). They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.

Source: Wikipedia

Papers

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Showing 13851–13900 of 17610 papers

TitleStatusHype
Winner Team Mia at TextVQA Challenge 2021: Vision-and-Language Representation Learning with Pre-trained Sequence-to-Sequence Model—0
CharacterChat: Supporting the Creation of Fictional Characters through Conversation and Progressive Manifestation with a Chatbot—0
Clinical Named Entity Recognition using Contextualized Token Representations—0
Combining Analogy with Language Models for Knowledge ExtractionCode0
Data Augmentation for Opcode Sequence Based Malware Detection—0
A Case Study in Bootstrapping Ontology Graphs from Textbooks—0
End-to-End Task-Oriented Dialog Modeling with Semi-Structured Knowledge ManagementCode0
Prompt Tuning or Fine-Tuning - Investigating Relational Knowledge in Pre-Trained Language ModelsCode0
Structured in Space, Randomized in Time: Leveraging Dropout in RNNs for Efficient Training—0
Membership Inference on Word Embedding and Beyond—0
A Discriminative Entity-Aware Language Model for Virtual Assistants—0
Ad Text Classification with Transformer-Based Natural Language Processing Methods—0
A Brief Study on the Effects of Training Generative Dialogue Models with a Semantic lossCode0
MaxUp: Lightweight Adversarial Training With Data Augmentation Improves Neural Network Training—0
Transitional Adaptation of Pretrained Models for Visual Storytelling—0
Low Resource German ASR with Untranscribed Data Spoken by Non-native Children -- INTERSPEECH 2021 Shared Task SPAPL System—0
Label prompt for multi-label text classification—0
Process for Adapting Language Models to Society (PALMS) with Values-Targeted Datasets—0
Learning to Complete Code with Sketches—0
An Improved Single Step Non-autoregressive Transformer for Automatic Speech Recognition—0
On Anytime Learning at MacroscaleCode0
SEOVER: Sentence-level Emotion Orientation Vector based Conversation Emotion Recognition Model—0
Augmented Neural Story Generation with Commonsense Inference—0
Algorithm to Compilation Co-design: An Integrated View of Neural Network Sparsity—0
Discrete Auto-regressive Variational Attention Models for Text ModelingCode0
Bilateral Personalized Dialogue Generation with Contrastive LearningCode0
Dialectal Speech Recognition and Translation of Swiss German Speech to Standard German Text: Microsoft's Submission to SwissText 2021—0
ASR Adaptation for E-commerce Chatbots using Cross-Utterance Context and Multi-Task Language Modeling—0
PairConnect: A Compute-Efficient MLP Alternative to Attention—0
Overcoming Domain Mismatch in Low Resource Sequence-to-Sequence ASR Models using Hybrid Generated Pseudotranscripts—0
SAS: Self-Augmentation Strategy for Language Model Pre-trainingCode0
Is Einstein more agreeable and less neurotic than Hitler? A computational exploration of the emotional and personality profiles of historical persons—0
Differentiable Neural Architecture Search with Morphism-based Transformable Backbone Architectures—0
Cross-utterance Reranking Models with BERT and Graph Convolutional Networks for Conversational Speech Recognition—0
Predicting the Ordering of Characters in Japanese Historical Documents—0
Leveraging Pre-trained Language Model for Speech Sentiment Analysis—0
Generate, Annotate, and Learn: NLP with Synthetic TextCode0
Exploring Unsupervised Pretraining Objectives for Machine TranslationCode0
MST: Masked Self-Supervised Transformer for Visual Representation—0
Balanced End-to-End Monolingual pre-training for Low-Resourced Indic Languages Code-Switching Speech Recognition—0
Auto-tagging of Short Conversational Sentences using Natural Language Processing MethodsCode0
DGA-Net Dynamic Gaussian Attention Network for Sentence Semantic Matching—0
Interpretable and Low-Resource Entity Matching via Decoupling Feature Learning from Decision MakingCode0
Hash Layers For Large Sparse Models—0
Generating Hypothetical Events for Abductive InferenceCode0
Exploiting Language Relatedness for Low Web-Resource Language Model Adaptation: An Indic Languages StudyCode0
Pre-trained Language Model for Web-scale Retrieval in Baidu Search—0
Measuring and Improving BERT's Mathematical Abilities by Predicting the Order of Reasoning—0
RoSearch: Search for Robust Student Architectures When Distilling Pre-trained Language Models—0
Video Imprint—0
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All datasetsWikiText-103Penn Treebank (Word Level)enwik8The PileWikiText-2LAMBADAOne Billion WordText8Penn Treebank (Character Level)Hutter PrizeOpenWebTextSALMon

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Decay RNNValidation perplexity76.67—Unverified
2GRUValidation perplexity53.78—Unverified
3LSTMValidation perplexity52.73—Unverified
4LSTMTest perplexity48.7—Unverified
5Temporal CNNTest perplexity45.2—Unverified
6TCNTest perplexity45.19—Unverified
7GCNN-8Test perplexity44.9—Unverified
8Neural cache model (size = 100)Test perplexity44.8—Unverified
9Neural cache model (size = 2,000)Test perplexity40.8—Unverified
10GPT-2 SmallTest perplexity37.5—Unverified
#ModelMetricClaimedVerifiedStatus
1TCNTest perplexity108.47—Unverified
2Seq-U-NetTest perplexity107.95—Unverified
3GRU (Bai et al., 2018)Test perplexity92.48—Unverified
4R-TransformerTest perplexity84.38—Unverified
5Zaremba et al. (2014) - LSTM (medium)Test perplexity82.7—Unverified
6Gal & Ghahramani (2016) - Variational LSTM (medium)Test perplexity79.7—Unverified
7LSTM (Bai et al., 2018)Test perplexity78.93—Unverified
8Zaremba et al. (2014) - LSTM (large)Test perplexity78.4—Unverified
9Gal & Ghahramani (2016) - Variational LSTM (large)Test perplexity75.2—Unverified
10Inan et al. (2016) - Variational RHNTest perplexity66—Unverified
#ModelMetricClaimedVerifiedStatus
1LSTM (7 layers)Bit per Character (BPC)1.67—Unverified
2HypernetworksBit per Character (BPC)1.34—Unverified
3SHA-LSTM (4 layers, h=1024, no attention head)Bit per Character (BPC)1.33—Unverified
4LN HM-LSTMBit per Character (BPC)1.32—Unverified
5ByteNetBit per Character (BPC)1.31—Unverified
6Recurrent Highway NetworksBit per Character (BPC)1.27—Unverified
7Large FS-LSTM-4Bit per Character (BPC)1.25—Unverified
8Large mLSTMBit per Character (BPC)1.24—Unverified
9AWD-LSTM (3 layers)Bit per Character (BPC)1.23—Unverified
10Cluster-Former (#C=512)Bit per Character (BPC)1.22—Unverified
#ModelMetricClaimedVerifiedStatus
1Smaller Transformer 126M (pre-trained)Test perplexity33—Unverified
2OPT 125MTest perplexity32.26—Unverified
3Larger Transformer 771M (pre-trained)Test perplexity28.1—Unverified
4OPT 1.3BTest perplexity19.55—Unverified
5GPT-Neo 125MTest perplexity17.83—Unverified
6OPT 2.7BTest perplexity17.81—Unverified
7Smaller Transformer 126M (fine-tuned)Test perplexity12—Unverified
8GPT-Neo 1.3BTest perplexity11.46—Unverified
9Transformer 125MTest perplexity10.7—Unverified
10GPT-Neo 2.7BTest perplexity10.44—Unverified